Customer Support RAG Chatbot
Description
# Customer Support RAG Chatbot A Retrieval-Augmented Generation (RAG) chatbot trained on customer support documentation to assist users by answering queries and providing relevant support information. ## Features - Answers questions based on provided customer support…
About
# Customer Support RAG Chatbot A Retrieval-Augmented Generation (RAG) chatbot trained on customer support documentation to assist users by answering queries and providing relevant support information. ## Features - Answers questions based on provided customer support documentation - Responds with "I don't know" for…
Details
- Author
- Shantanu1711
- Downloads
- 175
- Categories
- AI
Jump to
- Answers questions based on provided customer support documentation.
- Responds with “I don’t know” for out-of-scope questions.
- User-friendly web interface built with Streamlit.
- Semantic search for relevant information retrieval.
- Context-aware responses using Hugging Face LLM.
- Web scraping for AngelOne documentation.
- PDF processing for insurance documents.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Customer Support RAG ChatbotCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Clone the repository, set up a Python virtual environment, install dependencies, and create a .env file with your Hugging Face API key. Gather documentation (scrape AngelOne docs or place insurance PDFs in docs/insurance), then run process_documents.py. Start the backend with python mcp_server.py and the frontend with streamlit run app.py. Open the deployed URL in a browser and type questions in the chat interface.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"customer support rag chatbot": {
"mcp-server-shantanu1711": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}
}McpServers
{
"mcp-server-shantanu1711": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}Customer Support RAG Chatbot
A Retrieval-Augmented Generation (RAG) chatbot trained on customer support documentation to assist users by answering queries and providing relevant support information.
Features
- Answers questions based on provided customer support documentation
- Responds with "I don't know" for questions outside the documentation scope
- User-friendly web interface
- Semantic search for relevant information
- Context-aware responses
- Web scraping support for AngelOne documentation
- PDF processing for insurance documents
Setup Instructions
1. Clone the repository:
git clone <repository-url>
cd <repository-name>
2. Create and activate a virtual environment:
```bash
python -m venv venv
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